RESUMO
Background: MHC class I (MHC-I) loss is frequent in non-small cell lung cancer (NSCLC) rendering tumor cells resistant to T cell lysis. NK cells kill MHC-I-deficient tumor cells, and although previous work indicated their presence at NSCLC margins, they were functionally impaired. Within, we evaluated whether NK cell and CD8 T cell infiltration and activation vary with MHC-I expression. Methods: We used single-stain immunohistochemistry (IHC) and Kaplan-Meier analysis to test the effect of NK cell and CD8 T cell infiltration on overall and disease-free survival. To delineate immune covariates of MHC-I-disparate lung cancers, we used multiplexed immunofluorescence (mIF) imaging followed by multivariate statistical modeling. To identify differences in infiltration and intercellular communication between IFNγ-activated and non-activated lymphocytes, we developed a computational pipeline to enumerate single cell neighborhoods from mIF images followed by multivariate discriminant analysis. Results: Spatial quantitation of tumor cell MHC-I expression revealed intra- and inter-tumoral heterogeneity, which was associated with the local lymphocyte landscape. IHC analysis revealed that high CD56 + cell numbers in patient tumors were positively associated with disease-free survival (DFS) (HR=0.58, p =0.064) and over-all survival (OS) (HR=0.496, p =0.041). The OS association strengthened with high counts of both CD56 + and CD8 + cells (HR=0.199, p <1×10 -3 ). mIF imaging and multivariate discriminant analysis revealed enrichment of both CD3 + CD8 + T cells and CD3 - CD56 + NK cells in MHC-I-bearing tumors (p<0.05). To infer associations of functional cell states and local cell-cell communication, we analyzed spatial single cell neighborhood profiles to delineate the cellular environments of IFNγ +/- NK cells and T cells. We discovered that both IFNγ + NK and CD8 T cells were more frequently associated with other IFNγ + lymphocytes in comparison to IFNγ - NK cells and CD8 T cells (p<1×10 -30 ). Moreover, IFNγ + lymphocytes were most often found clustered near MHC-I + tumor cells. Conclusions: Tumor-infiltrating NK cells and CD8 T cells jointly affected control of NSCLC tumor progression. Co-association of NK and CD8 T cells was most evident in MHC-I-bearing tumors, especially in the presence of IFNγ. Frequent co-localization of IFNγ + NK cells with other IFNγ + lymphocytes in near-neighbor analysis suggests NSCLC lymphocyte activation is coordinately regulated. What is already known on this topic: MHC-I loss occurs frequently in NSCLC and corresponds with waning immunity in the tumor microenvironment (TME). NK cells recognize "missing-self" targets and could be leveraged to target NSCLC tumors with MHC-I loss. While NK cell presence at tumor margins has been documented in NSCLC, they were shown to lose function in this environment. What this study adds: We developed spatial analysis pipelines leveraging the local heterogeneity of the TME at single cell resolution to test whether NK cells and T cells together contribute antitumoral immunity in NSCLC. We discovered that a high density of tumor-infiltrating NK cells corresponded with DFS, and this association was increased in patients with high coincident CD8 T cells, especially those in central tumor. Intriguingly, both cell types were found clustered together in MHC-I-bearing tumors, especially when both expressed IFNγ, suggesting coordinated lymphocyte activities may enhance immune control of NSCLC. How this study might affect research practice or policy: This study provides a rationale for developing novel immunotherapies that simultaneously increase NK and T cell anti-tumoral immunity. Associations linking NK cells with patient survival and increased immune effector activity in NSCLC, even in MHC-I-deficient tumors, further highlights the need to devise and deploy NK cell activating strategies which may be highly efficacious in CD8 T cell refractory NSCLC.
RESUMO
Transplacental antibody transfer is crucially important in shaping neonatal immunity. Recently, prenatal maternal immunization has been employed to boost pathogen-specific immunoglobulin G (IgG) transfer to the fetus. Multiple factors have been implicated in antibody transfer, but how these key regulators work together to elicit selective transfer is pertinent to engineering vaccines for mothers to optimally immunize their newborns. Here, we present the first quantitative mechanistic model to uncover the determinants of placental antibody transfer and inform personalized immunization approaches. We identified placental FcγRIIb expressed by endothelial cells as a limiting factor in receptor-mediated transfer, which plays a key role in promoting preferential transport of subclasses IgG1, IgG3, and IgG4, but not IgG2. Integrated computational modeling and in vitro experiments reveal that IgG subclass abundance, Fc receptor (FcR) binding affinity, and FcR abundance in syncytiotrophoblasts and endothelial cells contribute to inter-subclass competition and potentially inter- and intra-patient antibody transfer heterogeneity. We developed an in silico prenatal vaccine testbed by combining a computational model of maternal vaccination with this placental transfer model using the tetanus, diphtheria, and acellular pertussis (Tdap) vaccine as a case study. Model simulations unveiled precision prenatal immunization opportunities that account for a patient's anticipated gestational length, placental size, and FcR expression by modulating vaccine timing, dosage, and adjuvant. This computational approach provides new perspectives on the dynamics of maternal-fetal antibody transfer in humans and potential avenues to optimize prenatal vaccinations that promote neonatal immunity.
